Minwise hashing (MinHash) [1]}, [2]}, [3]}, [4]}, [5]} is a standard technique for efficiently estimating the Jaccard similarity in massive binary data. Classical MinHash requires applying \(K\)  (independent) random permutations on each data vector to produce \(K\)  hash values. The recent work by [6]} proposed using just two random permutations: an initial permutation breaks whatever structures in the data vector and a second permutation is re-used \(K\)  times to generate \(K\)  hashes. They proved their surprising (and rather involved) theoretical finding that the estimation variance in their scheme is actually strictly smaller than the variance of the original MinHash.
